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Research on the Potential of Forestry’s Carbon-Neutral Contribution in China from 2021 to 2060

Sustainability (Switzerland), ISSN: 2071-1050, Vol: 14, Issue: 9
2022
  • 12
    Citations
  • 0
    Usage
  • 23
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    12
    • Citation Indexes
      12
  • Captures
    23

Article Description

Forest ecosystems play a crucial role in mitigating climate change. To assess and quantify the specific emissions reduction benefits of forest carbon sequestration, this study used a combina-tion of backpropagation neural networks, biomass conversion factor method, and logistic models to predict the carbon-neutral contribution from existing forests, planned afforestation, and forest tend-ing activities in China from 2021 to 2060. The results showed that (1) the emissions reduction contribution of forestry pathways in China was 7.91% (8588.61 MtCO2) at the carbon peak stage and 8.71% (24,932.73 MtCO2) at the carbon-neutral stage; (2) the southwest was the main contributing region, while the east and north lagged; (3) afforestation activities made the largest emission reduction contribution during the forecast period, while the contribution of existing forests continued to decline; and (4) carbon sequestration contribution by different forest origins was comparable during the carbon peak, while the contribution of plantation forests was expected to surpass that of natural forests during the carbon-neutral period. In order to maximize the benefits of the carbon-neutral pathway of forestry, it is necessary to enhance the policy frameworks related to forestry activities, forestry financial investment systems, and sustainable forest management systems to maximize the potential of this sector. Furthermore, more focus should be placed on reduction sectors to ensure the timely achievement of carbon goals and boost sustainable development in the context of climate change.

Bibliographic Details

Zheng Chen; Yue Hu; Jiaxi Cao; Ying Liu; Lumeng Xie; Shuhong Wu; Buddhi Dayananda; Brendan Fu; Ziwen Li; Ziyu Jia; Ye Chen

MDPI AG

Computer Science; Social Sciences; Energy; Environmental Science

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